1. METABOLOMICS IN THE STUDY OF MOLECULAR MEDICINE
ANBALAGAN MUTHY
LAVANIAN KANNAN
NG SHI TING
LEE YIE VEN
SITI NAJIHA BT BAHARUDDIN

2. SIMPLE DEFINITION
The systematic study of all metabolites in an organism and how they change in relation to a biological pertubation ( drug, disease or diet )

5. HISTORY
BC The first paper was titled, “Quantitative Analysis of Urine Vapor and Breath by Gas-Liquid Partition Chromatography”, by Robinson and Pauling in 1971.

6. IDENTIFICATION OF DISEASE BIOMARKERS
In clinical metabolomics one is almost always working with a biofluid or a fluidized tissue extract. The preference of working with biofluids over tissues is primarily dictated by the fact that fluids are far easier to process and analyze. Likewise the collection of biofluids is generally much less invasive than the collection of tissues. Biofluids analysis is always done with the assumption that the chemicals found in different biofluids are largely reflective of the biological state of the organ that produces or is bathed in this fluid. 

7. Metabolomic studies in order to diagnose diseases are quite conducted for other diseases . The idea that chemical components resulting cellular activities can reflect the health status of an individual : in China ( BC), doctors used ants to detect the presence of glucose in urine of patients with suspected diabetes . The pathophysiological changes observed from metabolomic have a shorter response time in comparison to the development of clinical symptoms for example. This is one of the reasons why metabolomics has been used in an attempt to identify biomarkers for early stage disease, especially those of a chronic nature such as cancer and respiratory diseases, which usually have a later diagnosis.

8. APPLICATION OF CANCER FIELD
Multiple metabolic pathways and networks can now be traced by the flow of atoms through metabolites, known as isotopomer analysis. There are currently very few metabolomic studies in cancer science, despite this great need and potential. Reports regarding human neuroendocrine cancer, liver tumor and several other tumors have been published

9. Fig. shows an example of multiple metabolic products traced by metabolomics technique in 15 patients

10. Arakaki et al. described CoMet, a fully automated and general computational metabolomics method that uses a Systems Biology approach to predict the human metabolites which intracellular levels are more likely to be altered in cancer cells. The authors then prioritize the metabolites predicted to be lowered in cancer compared to normal cells as potential anticancer agents. They discovered eleven metabolites that either alone or in combination exhibit significant antiproliferative activity in Jurkat leukemia cells.
The use of metabolic profiling and correlation networks enabled a more thorough interpretation of this dataset. Fingerprint analysis identified single metabolites that showed concentration changes between phenotypes, while network analysis highlighted alterations to the relationships of paired metabolites between phenotypes. 

11. DIABETES
Based on the paper written by Altmaier et al. presents a bioinformatics analysis of what can be considered as a standard experimental setting of a preclinical drug testing experiment with two independent factors, “state” and “medication”.
Targeted quantitative metabolomics covering a wide range of more than 800 relevant metabolites were measured in blood plasma samples from healthy and diabetic mice under rosiglitazone (a member of thiazolidinedione) treatment.

12. NMR-based metabolomics coupled with sophisticated bioinformatics was shown capable of identifying rapid changes in global metabolite profiles in urine and plasma (treatment “fingerprints”) which may be linked to the well-documented early changes in hepatic insulin sensitivity following thiazolidinedione intervention in Type 2 diabetes mellitus.

13. METABOLOMICS IN DRUG DISCOVERY
Drug molecules generally act on specific targets at the cellular level, and upon binding to the receptors, they exert a desirable alteration of the cellular activities, regarded as the pharmaceutical effect
The ability to effectively predict if a chemical compound is “drug-like” or “nondrug-like” is, thus, a valuable tool in the design, optimization, and selection of drug candidates for development

15. Drug developers have long-mined small-molecule metabolism for the design of enzyme inhibitors chemically similar to their endogenous substrates. The approach has yielded many successes, including antimetabolites such a folate derivatives used in cancer therapy and the nucleoside analog prodrugs used for antiviral therapy.

16. IMPORTANCE OF METABOLOMICS
The non-invasive nature of metabolomics and its close link to the phenotype make it an ideal tool for the pharmaceutical, preventive healthcare, and agricultural industries among others. Biomarker discovery and drug safety screens are two examples where metabolomics has already enabled informed decision making. In the future, with the availablity of personalised metabolomics, we will potentially be able to track the trends of our own metabolome for personalised drugs and improved treatment strategies.

17. Benefit from metabolomics on various levels: from product and stress testing in food industries, e.g. control of pesticides and identification of potentially harmful bacterial strains, to research in agriculture (crop protection and engineering), medical diagnostics in healthcare, and future applications in personalised medicine resulting in personliased treatment strategies.

18. Databases and Data Analysis Tools
Databases of metabolites and metabolic reactions offer a wealth of information regarding the interaction of small molecules with biological systems, notably in relation with their chemical reactivity. We summarize all such metabolite and metabolic pathway resources which

19. TECHNIQUE : PCA
The Principal Component Analysis (PCA) is a frequently used method which is applied to extract the systematic variance in a data matrix. It helps to obtain an overview over dominant patterns and major trends in the data. The aim of PCA is to create a set of latent variables which is smaller than the set of original variables but still explains all the variance of the original variables. In mathematical terms, PCA transforms a number of correlated variables into a smaller number of uncorrelated variables, the so-called principal components.

20. TECHNIQUE : PLS
Partial Least Squares (PLS), also called Projection to Latent Structures, is a linear regression method that can be applied to establish a predictive model, even if the objects are highly correlated. The X variables (the predictors) are reduced to principal components, as are the Y variables (the dependents). The components of X are used to predict the scores on the Y components, and the predicted Y component scores are used to predict the actual values of the Y variables. In constructing the principal components of X, the PLS algorithm iteratively maximizes the strength of the relation of successive pairs of X and Y component scores by maximizing the covariance of each X-score with the Y variables.

21. TECHNIQUE : O-PLS
The Orthogonal Projections to Latent Structures (O-PLS) is a linear regression method similar to PLS. However, the interpretation of the models is improved because the structured noise is modeled separately from the variation common to X and Y. Therefore, the O-PLS loading and regression coefficients allow for a more realistic interpretation than PLS, which models the structured noise together with the correlated variation between X and Y.

22. HUMAN METABOLOME DATABASE ( HMDB)
Human Metabolomics Projects with a $7.5 million budget was launched in Canada in January 2005
HMBD is the public face of Human Metabolomics Project.
This project mandates to quantify (normal and abnormal ranges) and identify all metabolites in urine, CSF, plasma and white blood cells.
Represents the most complete bioinformatics and chemoinformatics medical information database.

23. Links chemistry to genetics
Links compound concentration with disease
Queries and compares newly identified compounds with existing compounds.
Stimulates the consequences of knock-outs or deletions on metabolic flux.
contains records for thousands of endogenous metabolites identified by literature surveys (PubMed, OMIM, OMMBID, text books), data mining (KEGG, Metlin, BioCyc) or experimental analyses performed on urine, blood, and cerebrospinal fluid samples.

24. THE SMALL MOLECULE DATABASE PATHWAY
These pathways describe small molecule metabolism or small-molecule processes that are specific to humans and fall into four different categories: (i) metabolic pathways; (ii) small-molecule disease pathways, (iii) small molecule drug pathways, and (iv) small molecule signalling pathways.
found in humans and it must contain at least five small molecules. 

25. TOXIN AND TARGET DATABASE
Primarily intended to be a database that links toxins with their biological targets.
The molecular interaction information is further supplemented with detailed descriptions of the toxin’s mechanism of action, its metabolism in the human body, its lethal or toxic dose levels, its potential carcinogenicity, exposure sources, symptoms or health effects and suggested treatment options.

26. THANK YOU!